code: def prepare_multitask_data() -> Tuple[MultiModalData, MultiModalData]:
      """Load data for multitask regression / classification pipeline"""
      ex_data = fedot_project_root().joinpath("examples/data")
      train_df = pd.read_csv(
          ex_data.joinpath(
              "train_synthetic_regression_classification.csv"
          )
      )
      test_df = pd.read_csv(
          ex_data.joinpath(
              "test_synthetic_regression_classification.csv"
          )
      )
  
      # Data for classification
      class_task = Task(TaskTypesEnum.classification)
      class_train = InputData(
          idx=np.arange(0, len(train_df)),
          features=np.array(train_df[["feature_1", "feature_2"]]),
          target=np.array(train_df["class"]),
          task=class_task,
          data_type=DataTypesEnum.table,
          supplementary_data=SupplementaryData(is_main_target=False),
      )
      class_test = InputData(
          idx=np.arange(0, len(test_df)),
          features=np.array(test_df[["feature_1", "feature_2"]]),
          target=None,
          task=class_task,
          data_type=DataTypesEnum.table,
          supplementary_data=SupplementaryData(is_main_target=False),
      )
  
      # Data for regression
      task = Task(TaskTypesEnum.regression)
      reg_train = InputData(
          idx=np.arange(0, len(train_df)),
          features=np.array(train_df[["feature_1", "feature_2"]]),
          target=np.array(train_df["concentration"]),
          task=task,
          data_type=DataTypesEnum.table,
      )
      reg_test = InputData(
          idx=np.arange(0, len(test_df)),
          features=np.array(test_df[["feature_1", "feature_2"]]),
          target=None,
          task=task,
          data_type=DataTypesEnum.table,
      )
  
      train_multimodal = MultiModalData(
          {
              "logit": class_train,
              "data_source_table/regression": reg_train,
          }
      )
  
      test_multimodal = MultiModalData(
          {
              "logit": class_test,
              "data_source_table/regression": reg_test,
          }
      )
      return train_multimodal, test_multimodal
  
file_path: FEDOT/examples/advanced/multitask_classification_regression.py
imports: from datetime import timedelta
  from typing import Tuple
  import numpy
  import pandas
  from golem.core.tuning.simultaneous import SimultaneousTuner
  from fedot.core.data.data import InputData
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.data.supplementary_data import SupplementaryData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.pipelines.tuning.tuner_builder import TunerBuilder
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.metrics_repository import RegressionMetricsEnum
  from fedot.core.repository.tasks import TaskTypesEnum, Task
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 2 for calling a global function with example usage: prepare_multitask_data() and returns: train_multimodal,  test_multimodal